Currently, fuzzy logic has been combined successfully with many different analysis procedures, most notably with neural networks in the form of the adaptive neuro-fuzzy inference system. In one approach, a method was developed to use composed observations to parameterize membership functions (MF) of a fuzzy inference system (FIS). For example, it is known to use expert knowledge to create membership functions about composed patterns that map to qualitative features such as hot, cold, high, low, etc. In another approach, it is known to partition membership function parameterization. In fuzzy partitioning, the data space is partitioned into regions and membership functions are created about the centers of these regions. A similar approach is also known and implemented in unsupervised clustering algorithms, such as fuzzy c-means and clustering, which centers the membership functions on composed cluster centers and calculates the cluster parameters in terms of the distance from the cluster center. In yet another known approach, the parameters of the membership functions can be determined by performing least squares optimization of the fuzzy inference system inputs and outputs.
Finally, another known approach proposed a fuzzy instance model (FIM) which centers membership functions on exemplar observations and requires the membership function parameter(s) to be optimized according to some objective function.
Hence, there is a need to pioneer a new inference method and system by building it from the ground up instead of incrementally innovating on prior inference technologies. Additionally, there is a need for a method and system that ameliorates or overcomes one or more of the shortcomings of the known prior art.